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LudoBench
LLMs as Rules Oracles: Exploring Real-World Multimodal Reasoning in Tabletop Strategy Game Environments
ICLR 2026
A multimodal board-game reasoning benchmark evaluating LLM/VLM reasoning across 5 strategy games and 3 difficulty tiers.
Fields
| Field | Description |
|---|---|
ID |
Unique question identifier |
Game |
Board game name |
tier |
Difficulty tier (1, 2, or 3) |
Question |
The question text |
Answer |
Expected answer |
game_state_url |
Path(s) to game state image(s), semicolon-separated if multiple |
Benchmark Results
See benchmark_results.csv for accuracy scores of 9 models across all game/tier/modality splits.
Citation
@inproceedings{peper2026ludobench,
title={{LLMs} as Rules Oracles: Exploring Real-World Multimodal Reasoning in Tabletop Strategy Game Environments},
author={Peper, Joseph J. and Gandra, Sai Krishna and Zhang, Yunxiang and Chennareddy, Vaibhav and Jha, Shloki and Payani, Ali and Wang, Lu},
booktitle={Proceedings of the Fourteenth International Conference on Learning Representations (ICLR)},
year={2026},
address={Rio de Janeiro, Brazil}
}
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638
annotated question-answer pairs
5 games
: Kingdomino, Res Arcana, Pax Renaissance, Carcassonne, Catan
3 tiers
: Environment Perception (T1), Rules Integration (T2), Short-Horizon Optimization (T3)
3 rules modalities
: None (parametric), Text (text rulebook), Image (image rulebook)
Total file size:
1.24 GB